--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Mon Nov 21 11:12:59 WET 2016
codeml.models=0 1 2 3 7 8
mrbayes.mpich=
mrbayes.ngen=1000000
tcoffee.alignMethod=CLUSTALW2
tcoffee.params=
tcoffee.maxSeqs=0
codeml.bin=codeml
mrbayes.tburnin=2500
codeml.dir=
input.sequences=
mrbayes.pburnin=2500
mrbayes.bin=mb_adops
tcoffee.bin=t_coffee_ADOPS
mrbayes.dir=/usr/bin/
tcoffee.dir=
tcoffee.minScore=3
input.fasta=/opt/ADOPS/3/ACC-PA/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/3/ACC-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/ACC-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run2.p":
(Use the harmonic mean for Bayes factor comparisons of models)

(Values are saved to the file /opt/ADOPS/3/ACC-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1     -27181.50        -27199.41
2     -27180.94        -27200.89
--------------------------------------
TOTAL   -27181.18        -27200.41
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/3/ACC-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/ACC-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run2.p":
Summaries are based on a total of 3002 samples from 2 runs.
Each run produced 2001 samples of which 1501 samples were included.
Parameter summaries saved to file "/opt/ADOPS/3/ACC-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         1.367052    0.001374    1.295392    1.437846    1.367623   1145.49   1294.57    1.000
r(A<->C){all}   0.076491    0.000031    0.065520    0.087155    0.076484   1109.57   1178.82    1.000
r(A<->G){all}   0.313719    0.000132    0.292233    0.336622    0.313664    726.38    801.23    1.000
r(A<->T){all}   0.117651    0.000073    0.100219    0.132995    0.117641    697.21    855.65    1.000
r(C<->G){all}   0.039814    0.000010    0.034035    0.046258    0.039728   1089.46   1185.48    1.000
r(C<->T){all}   0.387968    0.000148    0.364154    0.412316    0.387805    704.75    715.87    1.000
r(G<->T){all}   0.064356    0.000025    0.054700    0.074167    0.064440    826.32    984.77    1.000
pi(A){all}      0.221872    0.000020    0.213027    0.230319    0.221802    630.90    820.16    1.000
pi(C){all}      0.286470    0.000023    0.276865    0.295084    0.286480    952.43   1042.94    1.000
pi(G){all}      0.279060    0.000023    0.270004    0.288737    0.279092    858.66    882.99    1.000
pi(T){all}      0.212599    0.000017    0.204295    0.220242    0.212615    693.29    828.86    1.001
alpha{1,2}      0.104131    0.000012    0.097563    0.111219    0.104104   1063.23   1178.46    1.000
alpha{3}        8.697463    1.942946    6.302670   11.574470    8.572163   1501.00   1501.00    1.000
pinvar{all}     0.316242    0.000177    0.290012    0.342428    0.316307   1407.62   1454.31    1.000
------------------------------------------------------------------------------------------------------
* Convergence diagnostic (ESS = Estimated Sample Size); min and avg values
correspond to minimal and average ESS among runs.
ESS value below 100 may indicate that the parameter is undersampled.
+ Convergence diagnostic (PSRF = Potential Scale Reduction Factor; Gelman
and Rubin, 1992) should approach 1.0 as runs converge.


Setting sumt conformat to Simple



 --- CODEML SUMMARY

Model 1: NearlyNeutral	-25004.541935
Model 2: PositiveSelection	-25004.54212
Model 0: one-ratio	-25175.679924
Model 3: discrete	-24947.134825
Model 7: beta	-24958.012288
Model 8: beta&w>1	-24948.97525


Model 0 vs 1	342.27597799999785

Model 2 vs 1	3.699999942909926E-4

Model 8 vs 7	18.074076000004425

Additional information for M7 vs M8:
Naive Empirical Bayes (NEB) analysis
Bayes Empirical Bayes (BEB) analysis (Yang, Wong & Nielsen 2005. Mol. Biol. Evol. 22:1107-1118)
Positively selected sites (*: P>95%; **: P>99%)
(amino acids refer to 1st sequence: D_melanogaster_ACC-PA)

            Pr(w>1)     post mean +- SE for w

    34 S      0.901         1.413 +- 0.271
    46 G      0.624         1.155 +- 0.459
    70 P      0.988*        1.491 +- 0.088
    73 P      0.872         1.386 +- 0.308
   899 S      0.617         1.142 +- 0.469
   962 L      0.822         1.344 +- 0.347